Electroencephalogram　(EEG)　signals　have　a　long　history　of　use　as　a　noninvasive　approach　to　measure　brain　function.　An　essential　component　in　EEG-based　applications　is　the　removal　of　Ocular　Artifacts　(OA)　from　the　EEG　signals.　In　this　paper　we　propose　a　hybrid　de-noising　method　combining　Discrete　Wavelet　Transformation　(DWT)　and　an　Adaptive　Predictor　Filter　(APF).　A　particularly　novel　feature　of　the　proposed　method　is　the　use　of　the　APF　based　on　an　adaptive　autoregressive　model　for　prediction　of　the　waveform　of　signals　in　the　ocular　artifact　zones.　In　our　test,　based　on　simulated　data,　the　accuracy　of　noise　removal　in　the　proposed　model　was　significantly　increased　when　compared　to　existing　methods　including:　Wavelet　Packet　Transform　(WPT)　and　Independent　Component　Analysis　(ICA),　Discrete　Wavelet　Transform　(DWT)　and　Adaptive　Noise　Cancellation　(ANC).　The　results　demonstrate　that　the　proposed　method　achieved　a　lower　mean　square　error　and　higher　correlation　between　the　original　and　corrected　EEG.　The　proposed　method　has　also　been　evaluated　using　data　from　calibration　trials　for　the　Online　Predictive　Tools　for　Intervention　in　Mental　Illness　(OPTIMI)　project.　The　results　of　this　evaluation　indicate　an　improvement　in　performance　in　terms　of　the　recovery　of　true　EEG　signals　with　EEG　tracking　and　computational　speed　in　the　analysis.　The　proposed　method　is　well　suited　to　applications　in　portable　environments　where　the　constraints　with　respect　to　acceptable　wearable　sensor　attachments　usually　dictate　single　channel　devices.